Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A heating, ventilation and air conditioning (HVAC) system fault detection method for optimizing fault detection and diagnosis, comprising: providing an electronic data gathering device configured to acquire data from one or more components associated with the heating, ventilation and air conditioning system; receiving, at a server, a plurality of signals indicative of sensed HVAC system operating parameters based on data acquired by the electronic data gathering device; identifying, at the server coupled to a database, a sensed operating parameter of the plurality of the signals indicative of sensed HVAC system operating parameters that exceeds a parameter threshold to determine a set of error parameters; normalizing, at the server, the sensed operating parameter according to a defined scale; determining, at the server, from the set of error parameters, a potential fault and a corresponding fault threshold; multiplying, at the server, each error parameter by a predetermined weighting factor to generate a set of weighted error parameters; summing, at the server, the set of weighted error parameters to generate a summed value; confirming, at the server, that the potential fault is a detected fault in response to a determination that the summed value exceeds the corresponding fault threshold; storing, in the database coupled to the server, a dataset including a set of optimization parameters comprising the parameter threshold, the predetermined weighting factor, and the corresponding fault threshold; applying an adjustment to the server to improve accuracy of the fault detection and diagnosis; and performing the applying step periodically based on a predetermined time interval or on a predetermined number of instances of receiving information related to the potential fault.
2. The HVAC fault detection method in accordance with claim 1 , wherein the set of optimization parameters further comprises parameters selected from the group consisting of sensed HVAC system operating parameters, the set of error parameters, and the detected fault.
This invention relates to HVAC fault detection systems, specifically improving the accuracy and efficiency of identifying faults in HVAC systems. The method addresses the challenge of detecting faults in HVAC systems by using an optimization process that adjusts parameters to minimize errors in system performance. The optimization parameters include sensed HVAC system operating parameters, such as temperature, pressure, and airflow, as well as error parameters that quantify deviations from expected performance. Additionally, the method incorporates detected faults to refine the optimization process, ensuring that the system can adapt to new or recurring issues. By dynamically adjusting these parameters, the method enhances fault detection accuracy and reduces false positives, leading to more reliable HVAC system operation. The system continuously monitors and analyzes these parameters to identify and correct faults in real time, improving overall system efficiency and longevity. This approach ensures that HVAC systems operate within optimal conditions, minimizing energy waste and maintenance costs.
3. The HVAC fault detection method in accordance with claim 1 , further comprising transmitting a fault message indicative of the identified fault to a user device.
This invention relates to HVAC (heating, ventilation, and air conditioning) fault detection systems. The technology addresses the problem of identifying and reporting faults in HVAC systems to ensure timely maintenance and prevent system failures. The method involves monitoring HVAC system performance data, such as temperature, pressure, airflow, and energy consumption, to detect anomalies or deviations from expected operating conditions. When a fault is identified, the system generates a fault message that includes details about the detected fault, such as its type, severity, and location within the HVAC system. This fault message is then transmitted to a user device, such as a smartphone, tablet, or computer, to alert the user or maintenance personnel. The transmission may occur via wireless communication protocols like Wi-Fi, Bluetooth, or cellular networks, ensuring real-time notifications. The system may also prioritize faults based on severity to guide maintenance actions. By providing immediate fault notifications, the invention improves HVAC system reliability, reduces downtime, and enhances energy efficiency. The method can be integrated into existing HVAC control systems or deployed as a standalone monitoring solution.
4. The HVAC fault detection method in accordance with claim 1 , further comprising providing an initial set of parameter thresholds.
The HVAC fault detection method addresses the challenge of identifying operational faults in heating, ventilation, and air conditioning (HVAC) systems by analyzing system parameters. The method involves monitoring multiple parameters of the HVAC system, such as temperature, pressure, airflow, and energy consumption, to detect deviations that may indicate faults. These parameters are continuously or periodically measured and compared against predefined thresholds to identify anomalies. The method also includes providing an initial set of parameter thresholds, which serve as baseline values for determining whether a measured parameter falls outside acceptable operating ranges. These thresholds may be adjusted over time based on system performance data or environmental conditions to improve accuracy. The method further involves generating alerts or triggering corrective actions when faults are detected, ensuring timely maintenance and reducing system downtime. By continuously monitoring and analyzing HVAC system parameters against these thresholds, the method enhances system reliability and efficiency.
5. The HVAC fault detection method in accordance with claim 1 , further comprising performing noise reduction on at least one of the received signals.
This invention relates to HVAC (heating, ventilation, and air conditioning) fault detection systems, specifically addressing the challenge of accurately identifying faults in HVAC systems by analyzing sensor signals. The method involves receiving signals from sensors monitoring HVAC system parameters such as temperature, pressure, airflow, or energy consumption. These signals are processed to detect anomalies indicative of faults, such as sensor malfunctions, component failures, or system inefficiencies. The method further includes performing noise reduction on the received signals to improve fault detection accuracy. Noise reduction techniques may involve filtering, signal smoothing, or statistical analysis to remove irrelevant variations and enhance the clarity of fault indicators. By reducing noise, the system can more reliably distinguish between normal operational fluctuations and genuine faults, leading to more precise and timely fault detection. This approach helps maintain HVAC system efficiency, reduce downtime, and prevent costly repairs by identifying issues early. The method is applicable to both residential and commercial HVAC systems, where accurate fault detection is critical for performance and energy savings.
6. The HVAC fault detection method in accordance with claim 1 , further comprising: receiving, at the server, feedback data indicative of whether the detected fault is an actual fault; and storing, in the dataset, the feedback data.
This invention relates to HVAC fault detection systems, specifically improving the accuracy and reliability of fault detection by incorporating user feedback. The system addresses the problem of false positives in automated HVAC fault detection, where non-fault conditions may be incorrectly identified as faults, leading to unnecessary maintenance or system downtime. The method involves a server that detects potential faults in HVAC systems based on sensor data or performance metrics. When a fault is detected, the system receives feedback from users or technicians indicating whether the detected fault was accurate or a false positive. This feedback data is then stored in a dataset to refine future fault detection algorithms. By continuously updating the dataset with verified fault information, the system improves its ability to distinguish between actual faults and normal operating conditions. The feedback loop ensures that the fault detection model adapts over time, reducing false alarms and increasing system reliability. This approach enhances maintenance efficiency by focusing resources on genuine issues while minimizing disruptions caused by incorrect fault alerts. The stored feedback data can also be used to identify recurring issues or patterns, enabling proactive maintenance strategies.
7. The HVAC fault detection method in accordance with claim 1 , further comprising: selecting, from the database, a plurality of datasets having a common detected fault; identifying, within the selected plurality of datasets, each unique set of optimization parameters; obtaining, for each of the selected plurality of datasets, a weighted total sum of the optimization parameters; determining a z-score for each set of weighted total sums of the optimization parameters; identifying the set of optimization parameters having the most negative z-score; and utilizing the set of optimization parameters having the most negative z-score as predetermined weighting factors.
This invention relates to HVAC fault detection systems that analyze optimization parameters to identify and resolve faults. The problem addressed is the need for an automated, data-driven approach to determine optimal weighting factors for fault detection in HVAC systems, improving accuracy and efficiency. The method involves selecting multiple datasets from a database where a common fault has been detected. Within these datasets, each unique combination of optimization parameters is identified. For each dataset, a weighted total sum of the optimization parameters is calculated. A z-score is then determined for each set of weighted total sums, which measures how far each set deviates from the mean. The set of optimization parameters with the most negative z-score is selected, indicating it is the most statistically significant for the detected fault. This selected set is then used as predetermined weighting factors in the fault detection process, enhancing the system's ability to accurately identify and prioritize faults. By leveraging statistical analysis of historical fault data, this method improves the reliability of HVAC fault detection by dynamically adjusting weighting factors based on real-world performance. The approach reduces manual intervention and ensures that fault detection is optimized for specific system conditions.
8. The HVAC fault detection method in accordance with claim 7 , further comprising comparing the z-score of the set of optimization parameters having the most negative z-score to a threshold.
The invention relates to fault detection in heating, ventilation, and air conditioning (HVAC) systems. HVAC systems often experience performance degradation due to faults, such as sensor malfunctions, refrigerant leaks, or airflow restrictions, which can lead to inefficiency and increased energy consumption. Traditional fault detection methods rely on predefined thresholds or complex models, which may not adapt well to varying operating conditions or system configurations. The method involves monitoring optimization parameters of the HVAC system, such as energy consumption, temperature deviations, or pressure readings, to detect anomalies. A set of optimization parameters is selected, and their statistical significance is evaluated using z-scores, which measure how many standard deviations a parameter deviates from its expected value. The z-score calculation accounts for the mean and standard deviation of historical or expected parameter values, allowing for dynamic fault detection without fixed thresholds. The method further includes comparing the z-score of the most negatively deviating optimization parameter to a predefined threshold. If the z-score exceeds the threshold, a fault is detected. This approach enables early identification of system anomalies, improving maintenance efficiency and reducing downtime. The method is adaptable to different HVAC configurations and operating conditions, enhancing its reliability in real-world applications.
9. The HVAC fault detection method in accordance with claim 8 , further comprising transmitting an alert in response to the comparing.
The invention relates to fault detection in heating, ventilation, and air conditioning (HVAC) systems. HVAC systems are prone to inefficiencies and failures due to component degradation, sensor inaccuracies, or environmental factors, leading to increased energy consumption and reduced performance. The invention addresses this by providing a method to detect faults in HVAC systems by analyzing sensor data and comparing it to expected operational parameters. The method involves collecting sensor data from various components of the HVAC system, such as temperature, pressure, and airflow sensors. This data is then processed to identify deviations from normal operating conditions. The method includes comparing the collected sensor data against predefined thresholds or historical data to determine if a fault condition exists. If a fault is detected, the method further includes transmitting an alert to notify system operators or maintenance personnel, enabling timely corrective action. The alert may be sent via a communication network to a monitoring system or a user device, ensuring prompt response to potential issues. The method may also involve analyzing trends in the sensor data over time to predict impending failures before they occur, improving system reliability. By continuously monitoring and comparing sensor data against expected values, the invention enhances HVAC system efficiency and reduces downtime. The alert transmission ensures that faults are addressed quickly, minimizing energy waste and maintaining optimal indoor environmental conditions.
10. The HVAC fault detection method in accordance with claim 8 , further comprising inhibiting detection of the common detected fault in response to the comparing.
The HVAC fault detection method involves monitoring an HVAC system to identify common faults, such as sensor failures, refrigerant leaks, or compressor malfunctions. The method uses sensor data from the HVAC system to detect deviations from expected operating conditions, which may indicate a fault. Once a fault is detected, the method compares the detected fault against a predefined list of common faults to determine if it matches any known issues. If a match is found, the method inhibits further detection of that specific fault to prevent redundant alerts or unnecessary system interventions. This inhibition ensures that the system does not repeatedly flag the same fault, reducing false alarms and improving diagnostic efficiency. The method may also include additional steps, such as logging the detected fault, triggering maintenance alerts, or adjusting system parameters to mitigate the fault's impact. By focusing on common faults and suppressing redundant detections, the method enhances the reliability and accuracy of HVAC system diagnostics.
11. The HVAC fault detection method in accordance with claim 7 , further comprising: determining a mean of each set of weighted total sums of the optimization parameters; and determining a standard deviation of each set of weighted total sums of the optimization parameters.
This invention relates to HVAC fault detection, specifically improving the analysis of optimization parameters to identify system anomalies. The method addresses the challenge of accurately detecting faults in HVAC systems by analyzing statistical measures of optimization parameters, which are derived from system performance data. The method involves calculating a weighted total sum for each set of optimization parameters, where the weights are determined based on the relative importance of each parameter in fault detection. After computing these weighted sums, the method further analyzes them by determining the mean and standard deviation for each set. These statistical measures provide insights into the variability and central tendency of the optimization parameters, enhancing the ability to detect deviations that may indicate faults. The method leverages these statistical analyses to improve the reliability and accuracy of fault detection in HVAC systems, ensuring timely maintenance and reducing system downtime. The approach is particularly useful in complex HVAC systems where multiple parameters influence performance and fault conditions.
12. The HVAC fault detection method in accordance with claim 11 , wherein the z-score is computed in accordance with the formula z-score=(fault threshold—mean of each set of weighted total sums)/the standard deviation mean of each set of weighted total sums.
This invention relates to fault detection in heating, ventilation, and air conditioning (HVAC) systems. The method addresses the challenge of accurately identifying faults in HVAC operations by analyzing performance data to detect anomalies. The system collects operational data from HVAC components, such as temperature, pressure, and airflow measurements, and processes this data to generate weighted total sums for different sets of measurements. These sums are then used to compute a z-score, which quantifies deviations from expected performance. The z-score is calculated using the formula z-score = (fault threshold – mean of each set of weighted total sums) / (standard deviation of each set of weighted total sums). This statistical approach helps distinguish normal variations from true faults, improving diagnostic accuracy. The method may also involve comparing the z-score against predefined thresholds to trigger alerts or corrective actions when anomalies are detected. By leveraging statistical analysis, the system enhances fault detection reliability, reducing false positives and ensuring timely maintenance. The technique is applicable to various HVAC configurations and can be integrated into existing monitoring systems for real-time fault detection.
13. A heating, ventilation and air conditioning (HVAC) fault detection system for optimizing fault detection and diagnosis, comprising: an electronic data gathering device configured to acquire data from one or more components associated with the HVAC system; a server configured for receiving and analyzing a plurality of signals indicative of sensed HVAC system operating parameters from one or more sensors of an HVAC system and for transmitting the received plurality of signals indicative of sensed HVAC system operating parameters to a user device, said user device is operably connected to the server, wherein the server comprises: a database configured for storing the received plurality of signals indicative of sensed HVAC system operating parameters; a processor operatively coupled to the database; a memory operatively coupled to the processor and including a set of executable instructions which, when executed by the processor, cause the processor to: identify a sensed operating parameter of the plurality of the signals indicative of sensed HVAC system operating parameters that exceeds a parameter threshold to determine a set of error parameters; normalize the sensed operating parameter according to a defined scale; determine, from the set of error parameters, a potential fault and a corresponding fault threshold; multiply each error parameter by a predetermined weighting factor to generate a set of weighted error parameters; sum the set of weighted error parameters to generate a summed value; confirm that the potential fault is a detected fault in response to a determination that the summed value exceeds the corresponding fault threshold; store, in the database, a dataset including a set of optimization parameters comprising the parameter threshold, the predetermined weighting factor, and the corresponding fault threshold; apply an adjustment to the server to improve accuracy of the fault detection and diagnosis; and perform the applying periodically based on a predetermined time interval or on a predetermined number of instances of receiving information related to the potential fault.
This invention relates to a heating, ventilation, and air conditioning (HVAC) fault detection system designed to optimize fault detection and diagnosis. The system addresses the challenge of accurately identifying and diagnosing faults in HVAC systems by analyzing operating parameters and dynamically adjusting detection thresholds. The system includes an electronic data gathering device that collects data from HVAC components and a server that receives and processes signals from sensors monitoring HVAC system parameters. These signals are stored in a database and analyzed by a processor executing instructions to detect anomalies. The processor identifies operating parameters exceeding predefined thresholds, normalizes them, and calculates error parameters. Each error parameter is weighted and summed to determine if a fault exists. If the summed value surpasses a fault threshold, the system confirms the fault and stores optimization parameters, including thresholds and weighting factors. The system periodically adjusts these parameters to improve detection accuracy, either at fixed time intervals or after a set number of fault-related events. This approach enhances fault detection reliability by continuously refining the system's diagnostic criteria.
14. The HVAC fault detection system in accordance with claim 13 , wherein the memory includes executable instructions that further cause the processor to transmit a fault message indicative of the identified fault.
The HVAC fault detection system is designed to monitor and diagnose faults in heating, ventilation, and air conditioning (HVAC) systems. The system includes a processor and memory storing executable instructions that enable the processor to analyze sensor data from the HVAC system to detect faults. When a fault is identified, the system generates a fault message indicating the nature of the fault. This message can be transmitted to a user interface, a remote monitoring system, or a maintenance service to facilitate timely repairs. The system may also include additional components such as sensors, communication interfaces, and user interfaces to support fault detection and reporting. The fault detection process involves comparing sensor data against predefined thresholds or patterns to identify deviations that indicate potential malfunctions. The system aims to improve HVAC system reliability and efficiency by providing early fault detection and notification, reducing downtime and maintenance costs. The fault message may include details such as the type of fault, its severity, and recommended corrective actions. The system can be integrated into existing HVAC control systems or deployed as a standalone monitoring solution.
15. The HVAC fault detection system in accordance with claim 13 , wherein the memory includes executable instructions that further cause the processor to: identify, within the selected plurality of datasets, each unique set of optimization parameters; obtain, for each of the selected plurality of datasets, a weighted total sum of the optimization parameters; determine a z-score for each set of weighted total sums of the optimization parameters; identify the set of optimization parameters having the most negative z-score; and utilize the identified set of optimization parameters having the most negative z-score as predetermined weighting factors.
This invention relates to an HVAC fault detection system that analyzes optimization parameters to identify and utilize the most negatively deviating set as predetermined weighting factors. The system addresses the challenge of detecting faults in HVAC systems by leveraging statistical analysis of optimization parameters to improve fault detection accuracy. The system processes a plurality of datasets, each containing optimization parameters, and identifies unique sets of these parameters within the selected datasets. For each dataset, the system calculates a weighted total sum of the optimization parameters. It then computes a z-score for each set of weighted total sums, which measures the statistical deviation of each set from the mean. The set of optimization parameters with the most negative z-score is identified, indicating the most significant deviation from expected performance. This identified set is then used as predetermined weighting factors in the fault detection process, enhancing the system's ability to detect anomalies and faults in HVAC operations. The approach ensures that the most statistically significant deviations are prioritized, improving the reliability and efficiency of fault detection in HVAC systems.
16. The HVAC fault detection system in accordance with claim 15 , wherein the memory includes executable instructions that further cause the processor to compare the z-score of the set of optimization parameters having the most negative z-score to a threshold.
The HVAC fault detection system monitors and analyzes HVAC system performance to identify faults or inefficiencies. The system collects operational data from sensors and other components, then processes this data to generate optimization parameters that reflect system performance. These parameters are statistically analyzed to detect anomalies, with a focus on identifying deviations that may indicate faults. The system calculates z-scores for the optimization parameters, which measure how far each parameter deviates from expected values under normal operating conditions. A lower z-score indicates a greater deviation, suggesting a potential fault. The system compares the most negative z-score (the parameter with the greatest deviation) to a predefined threshold. If the z-score falls below this threshold, the system flags the corresponding parameter as a potential fault, triggering alerts or corrective actions. This threshold-based comparison ensures that only significant deviations are flagged, reducing false positives while maintaining system reliability. The system may also adjust the threshold dynamically based on historical data or environmental conditions to improve accuracy. This approach enables early detection of HVAC faults, allowing for proactive maintenance and improved energy efficiency.
17. The HVAC fault detection system in accordance with claim 15 , wherein the memory includes executable instructions that further cause the processor to transmit an alert in response to the comparing.
An HVAC fault detection system monitors and analyzes HVAC system performance to identify operational faults. The system includes sensors that collect data on system parameters such as temperature, pressure, airflow, and energy consumption. A processor analyzes this data to detect deviations from expected performance, indicating potential faults. The system compares the collected data against predefined thresholds or historical performance data to identify anomalies. When a fault is detected, the system generates an alert to notify maintenance personnel or system operators. The alert may be transmitted via a network to a remote monitoring station or a local display. The system may also log fault events for further analysis and troubleshooting. By continuously monitoring and analyzing HVAC performance, the system helps prevent system failures, reduces energy waste, and improves overall system efficiency. The alert transmission ensures timely intervention, minimizing downtime and maintenance costs.
18. The HVAC fault detection system in accordance with claim 15 , wherein the memory includes executable instructions that further cause the processor to compute the z-score in accordance with the formula z-score=(fault threshold—mean of each set of weighted total sums)/the standard deviation mean of each set of weighted total sums.
The HVAC fault detection system is designed to identify anomalies in HVAC system performance by analyzing operational data. The system addresses the challenge of detecting faults early to prevent system failures and improve energy efficiency. It processes sensor data from HVAC components, such as temperature, pressure, and airflow readings, to generate weighted total sums for different operational parameters. These sums are then used to compute statistical metrics, including the mean and standard deviation, to establish baseline performance. The system further includes a fault detection mechanism that calculates a z-score to quantify deviations from expected behavior. The z-score is computed using the formula z-score = (fault threshold—mean of each set of weighted total sums) / (the standard deviation of each set of weighted total sums). This statistical approach helps distinguish normal variations from potential faults by comparing observed data against predefined thresholds. If the z-score exceeds a certain threshold, the system flags a potential fault, triggering alerts or corrective actions. The system may also log fault events for further analysis and maintenance scheduling. By continuously monitoring and analyzing HVAC performance, the system enhances reliability and reduces downtime.
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March 17, 2020
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